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Brazilian wind energy generation potential using mixtures of Weibull distributions

Author

Listed:
  • dos Santos, Fábio Sandro
  • do Nascimento, Kerolly Kedma Felix
  • da Silva Jale, Jader
  • Xavier, Sílvio Fernando Alves
  • Ferreira, Tiago A.E.

Abstract

As concerns about the greenhouse effect and the resulting increase in carbon dioxide levels in the atmosphere continue to mount, there is an increasing need to curtail the use of fossil fuels for energy generation, especially given the ever-growing energy demands of the population. In this work, we applied the Weibull–Weibull distribution mixture to adjust the wind speed series of all 575 weather stations at six different heights for the 12 months of the year in the period from 01-01-2000 to 03-31-2022 measured in the hourly interval. We present the main results for the month with the lowest and the month with the highest incidence of wind speed in Brazil for the height of 120 m, which are the months of May and September, respectively. In estimating the Weibull–Weibull parameters, the Expectation–Maximization algorithm was used. Using the results obtained at each meteorological station and the Inverse Distance Weighting method, we predicted wind energy generation at points without information about wind speed. We observed that the Weibull–Weibull distribution mixture proved excellent for estimating wind energy density distribution over large areas We were able to provide a suitable fit for the different regions of Brazil. The findings indicate that the model employed has the potential to serve as a strong contender for adoption in other nations, enabling the integration of alternative energy sources in a complementary fashion. This measure could prove instrumental in mitigating the use of environmentally hazardous energy sources.

Suggested Citation

  • dos Santos, Fábio Sandro & do Nascimento, Kerolly Kedma Felix & da Silva Jale, Jader & Xavier, Sílvio Fernando Alves & Ferreira, Tiago A.E., 2024. "Brazilian wind energy generation potential using mixtures of Weibull distributions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    • Handle: RePEc:eee:rensus:v:189:y:2024:i:pb:s1364032123008481
    DOI: 10.1016/j.rser.2023.113990
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    as
    1. Bagci, Kubra & Arslan, Talha & Celik, H. Eray, 2021. "Inverted Kumarswamy distribution for modeling the wind speed data: Lake Van, Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    2. Eduardo Janser de Azevedo Dantas & Luiz Pinguelli Rosa & Neilton Fidelis da Silva & Marcio Giannini Pereira, 2019. "Wind Power on the Brazilian Northeast Coast, from the Whiff of Hope to Turbulent Convergence: The Case of the Galinhos Wind Farms," Sustainability, MDPI, vol. 11(14), pages 1-24, July.
    3. Cui, Guodong & Pei, Shufeng & Rui, Zhenhua & Dou, Bin & Ning, Fulong & Wang, Jiaqiang, 2021. "Whole process analysis of geothermal exploitation and power generation from a depleted high-temperature gas reservoir by recycling CO2," Energy, Elsevier, vol. 217(C).
    4. Rocha, Luiz Célio Souza & Aquila, Giancarlo & Rotela Junior, Paulo & Paiva, Anderson Paulo de & Pamplona, Edson de Oliveira & Balestrassi, Pedro Paulo, 2018. "A stochastic economic viability analysis of residential wind power generation in Brazil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 412-419.
    5. Santos, Fábio Sandro dos & Nascimento, Kerolly Kedma Felix do & Jale, Jader da Silva & Stosic, Tatijana & Marinho, Manoel H.N. & Ferreira, Tiago A.E., 2021. "Mixture distribution and multifractal analysis applied to wind speed in the Brazilian Northeast region," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    6. Vinhoza, Amanda & Schaeffer, Roberto, 2021. "Brazil's offshore wind energy potential assessment based on a Spatial Multi-Criteria Decision Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    7. Adinda Franky Nelwan & Rinaldy Dalimi & Chairul Hudaya, 2021. "A New Formula to Quantify the National Energy Security of the World s Top Ten Most Populous Nations," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 394-406.
    8. Carta, J.A. & Ramírez, P., 2007. "Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions," Renewable Energy, Elsevier, vol. 32(3), pages 518-531.
    9. Carneiro, Tatiane C. & Melo, Sofia P. & Carvalho, Paulo C.M. & Braga, Arthur Plínio de S., 2016. "Particle Swarm Optimization method for estimation of Weibull parameters: A case study for the Brazilian northeast region," Renewable Energy, Elsevier, vol. 86(C), pages 751-759.
    10. Hrnčić, Boris & Pfeifer, Antun & Jurić, Filip & Duić, Neven & Ivanović, Vladan & Vušanović, Igor, 2021. "Different investment dynamics in energy transition towards a 100% renewable energy system," Energy, Elsevier, vol. 237(C).
    11. Narasimha D. Rao & Jihoon Min & Alessio Mastrucci, 2019. "Energy requirements for decent living in India, Brazil and South Africa," Nature Energy, Nature, vol. 4(12), pages 1025-1032, December.
    12. Jacobson, Mark Z., 2021. "The cost of grid stability with 100 % clean, renewable energy for all purposes when countries are isolated versus interconnected," Renewable Energy, Elsevier, vol. 179(C), pages 1065-1075.
    13. Shin, Ju-Young & Ouarda, Taha B.M.J. & Lee, Taesam, 2016. "Heterogeneous mixture distributions for modeling wind speed, application to the UAE," Renewable Energy, Elsevier, vol. 91(C), pages 40-52.
    14. Perini de Souza, Noéle Bissoli & Cardoso dos Santos, José Vicente & Sperandio Nascimento, Erick Giovani & Bandeira Santos, Alex Alisson & Moreira, Davidson Martins, 2022. "Long-range correlations of the wind speed in a northeast region of Brazil," Energy, Elsevier, vol. 243(C).
    15. Carvalho, N.B. & Berrêdo Viana, D. & Muylaert de Araújo, M.S. & Lampreia, J. & Gomes, M.S.P. & Freitas, M.A.V., 2020. "How likely is Brazil to achieve its NDC commitments in the energy sector? A review on Brazilian low-carbon energy perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    16. Usman, Muhammad & Balsalobre-Lorente, Daniel & Jahanger, Atif & Ahmad, Paiman, 2022. "Pollution concern during globalization mode in financially resource-rich countries: Do financial development, natural resources, and renewable energy consumption matter?," Renewable Energy, Elsevier, vol. 183(C), pages 90-102.
    17. Khosravi, A. & Koury, R.N.N. & Machado, L. & Pabon, J.J.G., 2018. "Energy, exergy and economic analysis of a hybrid renewable energy with hydrogen storage system," Energy, Elsevier, vol. 148(C), pages 1087-1102.
    18. Nong, Duy & Simshauser, Paul & Nguyen, Duong Binh, 2021. "Greenhouse gas emissions vs CO2 emissions: Comparative analysis of a global carbon tax," Applied Energy, Elsevier, vol. 298(C).
    19. Vasconcellos, H.A.S. & Caiado Couto, L., 2021. "Estimation of socioeconomic impacts of wind power projects in Brazil's Northeast region using Interregional Input-Output Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    20. Tolmasquim, Maurício T. & de Barros Correia, Tiago & Addas Porto, Natália & Kruger, Wikus, 2021. "Electricity market design and renewable energy auctions: The case of Brazil," Energy Policy, Elsevier, vol. 158(C).
    21. Hu, Qinghua & Wang, Yun & Xie, Zongxia & Zhu, Pengfei & Yu, Daren, 2016. "On estimating uncertainty of wind energy with mixture of distributions," Energy, Elsevier, vol. 112(C), pages 935-962.
    22. Duarte Jacondino, William & Nascimento, Ana Lucia da Silva & Calvetti, Leonardo & Fisch, Gilberto & Augustus Assis Beneti, Cesar & da Paz, Sheila Radman, 2021. "Hourly day-ahead wind power forecasting at two wind farms in northeast Brazil using WRF model," Energy, Elsevier, vol. 230(C).
    23. Fragkos, Panagiotis & Laura van Soest, Heleen & Schaeffer, Roberto & Reedman, Luke & Köberle, Alexandre C. & Macaluso, Nick & Evangelopoulou, Stavroula & De Vita, Alessia & Sha, Fu & Qimin, Chai & Kej, 2021. "Energy system transitions and low-carbon pathways in Australia, Brazil, Canada, China, EU-28, India, Indonesia, Japan, Republic of Korea, Russia and the United States," Energy, Elsevier, vol. 216(C).
    24. Erika Winquist & Michiel Van Galen & Simon Zielonka & Pasi Rikkonen & Diti Oudendag & Lijun Zhou & Auke Greijdanus, 2021. "Expert Views on the Future Development of Biogas Business Branch in Germany, The Netherlands, and Finland until 2030," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    25. Deep, Sneh & Sarkar, Arnab & Ghawat, Mayur & Rajak, Manoj Kumar, 2020. "Estimation of the wind energy potential for coastal locations in India using the Weibull model," Renewable Energy, Elsevier, vol. 161(C), pages 319-339.
    26. Juárez, Alberto Aquino & Araújo, Alex Maurício & Rohatgi, Janardan Singh & de Oliveira Filho, Oyama Douglas Queiroz, 2014. "Development of the wind power in Brazil: Political, social and technical issues," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 828-834.
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